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IEEE Uttar Pradesh Section Conference on Electrical Computer and Electronics (UPCON)

作者： Chakraborty, Soumendu Singh, Satish Kumar Chakraborty, Pavan Indian Inst Informat Technol Allahabad 211012 Uttar Pradesh India

ISBN: (纸本)9781467385077

In this paper a generalized distance based local binary pattern (GDLBP) is proposed which achieves better recognition accuracy over LBP and significantly increases the recognition accuracy when combined with LVP. The proposed GDLBP is combined with local vector pattern (LVP) to achieve better recognition rates. Proposed GDLBP explores the spatial relationships of the neighboring pixels at different radii and inter radii distances. Proposed descriptors GDLBP and GDLBP-LVP have been analyzed and compared with the existing LBP and LVP over most challenging benchmark facial image databases LFW, and color FERET. Analysis of the descriptors shows that the proposed GDLBP and GDLBP-LVP achieves noticeably better results as compared to LBP and LVP.

关键词： local pattern descriptors local binary pattern local vector pattern generalized distance local binary pattern (GDLBP) face recognition

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Diagnosing skin cancer via C-means segmentation with enhanced fuzzy optimization

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IET IMAGE PROCESSING 2021年第10期15卷 2266-2280页

作者： Durgarao, Nagayalanka Sudhavani, Ghanta Acharya Nagarjuna Univ Dept ECE Guntur Andhra Pradesh India RVR & JC Coll Engn Dept ECE Guntur Andhra Pradesh India

The early detection of cancer decreases the death rate as well, but mostly the disorder symptoms are unpredictable. Numerous skin cancer detection techniques are available in the dice, yet the effectiveness remains unachieved. This paper aims to introduce a skin cancer detection technique that characterizes the nature of cancer: normal, benign or malignant. The proposed technique includes three stages like Segmentation, Feature Extraction, and Classification. Here, the Fuzzy C-means Clustering (FCM) is used to segment the given input image. Then, the features are mined from the segmented image using local vector pattern (LVP) and local Binary pattern (LBP). Subsequently, the Fuzzy classifier is used to do the classification process that gets the extracted features (LVP+LBP) as the input. The classifier outputs the nature of the image. As the primary contribution of this work, the limits of membership functions in the Fuzzy classifier are optimally selected by a new improved Rider Optimization Algorithm (ROA) termed as Distance Oriented ROA (DOROA). The performance of the proposed DOROA model is compared over other conventional models in terms of accuracy, sensitivity, specificity, precision, Negative Predictive Value (NPV), F1-score and Matthews correlation coefficient (MCC), False positive rate (FPR), False Negative Rate (FNR), and False Discovery Rate (FDR).

关键词： pattern clustering skin cancer detection techniques improved Rider Optimization Algorithm local vector pattern optimisation cancer skin skin cancer detection technique feature extraction enhanced fuzzy optimization image segmentation False Negative Rate disorder symptoms local Binary pattern classification process Feature Extraction fuzzy set theory medical image processing Fuzzy classifier death rate LVP+LBP image classification segmented image

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Optimized convolutional neural network for soft tissue sarcoma diagnosis

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MULTIMEDIA TOOLS AND APPLICATIONS 2023年第3期82卷 4497-4515页

作者： Kathavate, Pravin Narayan Amudhavel, J. Koneru Lakshmaiah Educ Fdn Dept CSE Vaddeswaram Andhra Pradesh India

Indistinct soft tissue sarcomas are a form of tumor that can be difficult to diagnose in a tremendous population. For earlier prediction of distant metastasis, some traditional classifications are suffered by technological issues, lack of enhancement methods, reliability, and so on. To provide a better classification, this paper introduces a new deep learning-based soft tissue sarcoma classification framework. Initially, spatial features and LVP features are *** main aim of this phase is to generate LVP using each pixel vector and provides the benefits of inherent structures in edge patches. The subsequent classification phase is utilized an optimized Convolutional Neural Network (CNN). Moreover, the weight and filter size of CNN will be optimally tuned by the new Self Adaptive Bat Algorithm (SA-BA). Finally, SA-BA method is compared over some existing classifiers in terms of various measures.

关键词： Soft tissue sarcoma local vector pattern Convolutional neural network Bat algorithm SA-BA model

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Optimal weighted hybrid pattern for content based medical image retrieval using modified spider monkey optimization

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INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2021年第2期31卷 828-853页

作者： Darapureddy, Nagadevi Karatapu, Nagaprakash Battula, Tirumula Krishna JNTU Chaitanya Bharathi Inst Technol Res Scholar Dept ECE Kakinada India Gudlavalleru Engn Coll Dept ECE Vijayawada Andhra Pradesh India Jawaharlal Nehru Technol Univ Dept ECE Kakinada India

The current approaches for image retrieval are more concentrating on numerous image features. Texture, shape, spatial information, and color are the fundamental features to deal with flexible image datasets. This paper aims to develop new Content-Based Image Retrieval System based on Optimal Weighted Hybrid pattern. Two relevant patters like local vector pattern and local Derivative pattern are intended to develop a novel Content-Based Image Retrieval system. The optimal weighted hybrid pattern is implemented to derive a new feature vector, so that the weight is optimized by a modified optimization algorithm called Improved local Leader-based Spider Monkey Optimization to maximize the precision and recall of the retrieved images. The retrieval of the image is done by measuring the similarity based on Mean Square Distance between the features of query image as well as training image. Finally, the performance comparison of the proposed and the traditional patterns shows its reliable performance.

关键词： content-based medical image retrieval improved local leader-based spider monkey optimization local derivative pattern local vector pattern machine learning-based classification novel descriptors optimal weighted hybrid pattern

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Detection of skin cancer with adaptive fuzzy classifier using improved whale optimization

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BIOMEDICAL ENGINEERING-BIOMEDIZINISCHE TECHNIK 2020年第5期65卷 605-619页

作者： Durgarao, Nagayalanka Sudhavani, Ghanta Acharya Nagarjuna Univ Dept ECE Guntur Andhra Pradesh India RVR & JC Coll Engn Dept ECE Guntur Andhra Pradesh India

Skin cancer is considered as a well-known type of cancer globally, and its occurrence has been found to be raised in current days. Researchers state that the disease requires early prediction so that the identification of precise signs will make it simple for the dermatologists and clinicians. This disorder has been established to be unpredictable. Hence, this paper intends to develop an efficient skin cancer detection scheme, which classifies the nature of cancer, whether it is normal, benign or malignant. Accordingly, the skin image which is given as input is segmented using k-means clustering model and the features are extracted from segmented image using local vector pattern (LVP). Moreover, the extracted features are subjected to fuzzy classifier for recognizing the cancer. In addition, the limits of membership functions are optimally selected by improved Whale Optimization Algorithm (WOA). Thus, the proposed scheme is termed as Improved Selection of Encircling and Spiral updating position of WO-based Fuzzy Classifier (ISESW-FC). From the optimized output, the type of skin cancer image can be determined, whether it is normal, benign or malignant. The performance of proposed model is compared over other conventional methods, and its efficiency is proved by means of Type I and Type II measures.

关键词： k-means clustering local vector pattern skin cancer whale optimization algorithm

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local directional gradient pattern: a local descriptor for face recognition

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MULTIMEDIA TOOLS AND APPLICATIONS 2017年第1期76卷 1201-1216页

作者： Chakraborty, Soumendu Singh, Satish Kumar Chakraborty, Pavan Indian Inst Informat Technol Dept Informat Technol Allahabad Uttar Pradesh India

In this paper a local pattern descriptor in high order derivative space is proposed for face recognition. The proposed local directional gradient pattern (LDGP) is a 1D local micropattern computed by encoding the relationships between higher order derivatives of the reference pixel in four distinct directions. The proposed descriptor identifies relationship between the high order derivatives of the referenced pixel in four different directions to compute the micropattern which corresponds to the local feature. Proposed descriptor considerably reduces the length of the micropattern which consequently reduces the extraction time and matching time while maintaining the recognition rate. Results of the extensive experiments conducted on benchmark databases AT&T, Extended Yale B and CMU-PIE show that the proposed descriptor significantly reduces the extraction as well as matching time while the recognition rate of the descriptor is almost similar to existing state of the art methods. Moreover the proposed descriptor is more resistant against the AWGN compared to the other state of the art descriptors used for face recognition problems.

关键词： local pattern descriptors local derivative pattern local vector pattern local directional gradient pattern Face recognition

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LVP extraction and triplet-based segmentation for diabetic retinopathy recognition

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EVOLUTIONARY INTELLIGENCE 2018年第1-2期11卷 117-129页

作者： Randive, Santosh Nagnath Rahulkar, Amol D. Senapati, Ranjan K. Koneru Lakshmaiah Educ Fdn Green Fields Dept Elect & Commun Engn Guntur 522502 Andhra Prades India NIT Farmagudi Goa India

Till now, the detection of diabetic retinopathy seems to be one of the sensitive research topics since it is related to health care of any individual. A number of contributions in terms of detection already exists in the dice;still, there present some problems regarding the detection accuracy. This issue motivates to develop a new detection model of diabetic retinopathy, and moreover, this model tells the severity of retinopathy from the given fundus image. The proposed model includes preprocessing, segmentation, feature extraction and classification stages. Here, Triplet Half band Filterbank (THFB) Segmentation is performed, local vector pattern (LVP) is used for extracting the features, principle component analysis (PCA) procedure is used to reduce the dimensions of the feature vector, and neural network (NN) is used for classification purpose. The proposed model compares its performance over other conventional classifiers like support vector machine (SVM), k nearest neighbor (k-NN) and Navies Bayes (NB) in terms of positive and negative measures. The positive measures are accuracy, specificity, sensitivity, precision, negative predictive value (NPV), F1-Score and Matthews Correlation Coefficient (MCC). Similarly, the negative measures are the false positive rate (FPR), false negative rate (FNR) and false discovery rate (FDR), and the efficiency of the proposed model is proven.

关键词： Diabetic retinopathy THFB segmentation local vector pattern Feature extraction Neural network classification

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Near duplicate detection of images with area and proposed pixel-based feature extraction

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2023年第2期35卷 e7477-e7477页

作者： Governor, Kalaiarasi Ramanujam, Padmavathy Mana, Suja Cherukullapurath Perumal, Geetha Sathyabama Inst Sci & Technol Chennai India New Prince Shri Bhavani Coll Engn & Technol Chennai India Sri Venkateswara Coll Engn Dept Comp Sci & Engn Kanchipuram India

Efficient and accurate near-duplicate recognition is the trendy research area. Identification of invalid near-duplicate images offers a wide range of applications, including digital picture forensics, web-scale retrieval, and, social media analysis. This article intends to introduce a novel near duplicate detection model of images that consists of two stages such as (i) feature extraction and (ii) similarity computation. Originally, the image database is subjected to extracting the features, in which the area-based features and pixel-based features are extracted. Here, the area-based feature extraction includes the contrast context histogram (CCH-descriptors) and improved weighted bag of visual word (w-BovW) features;the pixel-based feature extraction includes the texture features like the proposed local vector pattern. Once the query image is given as the input, it is subjected to the feature extraction stage. Then, the feature vector database and the extracted features of query images are evaluated under similarity computation via improved Jaccard similarity evaluation. Thus, the near duplicate detection of the image is obtained in an effective manner.

关键词： feature extraction feature vector database local vector pattern near duplicate detection similarity computation

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Enhanced ResNet-151-based fused features for optimized Bi-LSTM-DNN-aided handwritten character and digits recognition

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 244卷

作者： Srinivasa, Rao N. Nelson, Kennedy Babu C. Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept CSE Kanchipuram Chennai Rd Chennai 602105 Tamil Nadu India Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Cloud Comp Kanchipuram Chennai Rd Chennai 602105 Tamil Nadu India

Optical Character Recognition (OCR) is a method to convert a scanned photo of"handwritten character recognition (HCR) or printed character recognition (PCR)" into a form of digital text. HCR is a version of OCR, which is remarkably modeled to identify handwritten text, while PCR aims at printed text identification. The identification of handwritten characters and digits is more complicated as compared to PCR because of the diversities in human writing styles, stokes, thickness, and curves of characters. Similarly, achievements in several computer vision tasks consider the Convolutional Neural Networks (CNN) to give an end-to-end solution for HCR with huge success. However, the process of significant feature learning for the identification of images is complicated with little data. Hence, this paper aims to develop a new handwritten character and digit recognition model with the incorporation of a deep learning strategy. Initially, the data related to Indian languages are collected from the standard benchmark datasets. Then, the collected data are given into the feature extraction phase 1, where the ResNet 151 is used for extracting the feature set 1. Similarly, the data gathered are considered in the feature extraction phase 2, where the Optimal Ensemble pattern extraction approach is developed with local Binary pattern (LBP), local Gradient patterns (LGP), local Tetra pattern (LTrP), and local vector pattern (LVP) for extracting the significant patterns from the language data. These extracted patterns are given into the ResNet 151 for getting the feature set 2. Here, the features from ResNet 151 get optimized with the enhanced optimization algorithm with Fitness-based Sail Fish Optimizer (F-SFO). The obtained feature set 1 and optimal feature set 2 are concatenated for performing final recognition. At last, the HCR is done with the help of developed Bi-LSTM-DNN to achieve the enhanced and accurate recognition of handwritten characters of the Indian languages. The p

关键词： Handwritten character and digits recognition Resnet-151 Fitness-based sail fish optimizer local binary pattern local gradient pattern local tetra pattern local vector pattern Bidirectional-long short term memory Deep neural network

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Multimodal Features and Probability Extended Nearest Neighbor Classification for Content-Based Lecture Video Retrieval

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JOURNAL OF INTELLIGENT SYSTEMS 2017年第3期26卷 585-599页

作者： Waykar, Sanjay B. Bharathi, C. R. Vel Tech Dr RR & Dr SR Tech Univ Madras Tamil Nadu India

Due to the ever-increasing number of digital lecture libraries and lecture video portals, the challenge of retrieving lecture videos has become a very significant and demanding task in recent years. Accordingly, the literature presents different techniques for video retrieval by considering video contents as well as signal data. Here, we propose a lecture video retrieval system using multimodal features and probability extended nearest neighbor (PENN) classification. There are two modalities utilized for feature extraction. One is textual information, which is determined from the lecture video using optical character recognition. The second modality utilized to preserve video content is local vector pattern. These two modal features are extracted, and the retrieval of videos is performed using the proposed PENN classifier, which is the extension of the extended nearest neighbor classifier, by considering the different weightages for the first-level and second-level neighbors. The performance of the proposed video retrieval is evaluated using precision, recall, and F-measure, which are computed by matching the retrieved videos and the manually classified videos. From the experimentation, we proved that the average precision of the proposed PENN+VQ is 78.3%, which is higher than that of the existing methods.

关键词： Video retrieval lecture videos optical character recognition (OCR) texture local vector pattern classifier precision

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